





from __future__ import annotations





def 	lowerCAmelCase_					(			__a						)	->						bool:







  """simple docstring"""





  lowerCamelCase__: Union[str, Any]         =len(__a						)
  # We need to create solution object to save path.
  lowerCamelCase__: Optional[Any]         =[[0 for _ in range(__a						)] for _ in range(__a						)]
  lowerCamelCase__: int         =run_maze(__a      ,       0      ,       0      ,       __a						)
  if solved:
    print("\n".join(str(__a						) for row in solutions						)						)
  else:
    print("No solution exists!"						)
  return solved





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a      ,       __a						)	->						bool:







  """simple docstring"""





  lowerCamelCase__: List[str]         =len(__a						)
  # Final check point.
  if i == j == (size - 1):
    lowerCamelCase__: List[str]         =1
    return True

  lowerCamelCase__: Optional[int]         =(not i < 0) and (not j < 0)  # Check lower bounds
  lowerCamelCase__: Dict         =(i < size) and (j < size)  # Check upper bounds

  if lower_flag and upper_flag:
    # check for already visited and block points.
    lowerCamelCase__: Optional[Any]         =(not solutions[i][j]) and (not maze[i][j])
    if block_flag:
      # check visited
      lowerCamelCase__: Optional[Any]         =1

      # check for directions
      if (
          run_maze(__a      ,       i + 1      ,       __a      ,       __a						)
          or run_maze(__a      ,       __a      ,       j + 1      ,       __a						)
          or run_maze(__a      ,       i - 1      ,       __a      ,       __a						)
          or run_maze(__a      ,       __a      ,       j - 1      ,       __a						)
      ):
        return True

      lowerCamelCase__: List[Any]         =0
      return False
  return False


if __name__ == "__main__":
     import doctest

     doctest.testmod()


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a    :	Tuple         =    {
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a    :	Dict         =    [
		    """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LukeForEntityClassification""",
		    """LukeForEntityPairClassification""",
		    """LukeForEntitySpanClassification""",
		    """LukeForMultipleChoice""",
		    """LukeForQuestionAnswering""",
		    """LukeForSequenceClassification""",
		    """LukeForTokenClassification""",
		    """LukeForMaskedLM""",
		    """LukeModel""",
		    """LukePreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
		from .tokenization_luke import LukeTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_luke import (
				    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LukeForEntityClassification,
				    LukeForEntityPairClassification,
				    LukeForEntitySpanClassification,
				    LukeForMaskedLM,
				    LukeForMultipleChoice,
				    LukeForQuestionAnswering,
				    LukeForSequenceClassification,
				    LukeForTokenClassification,
				    LukeModel,
				    LukePreTrainedModel,
				)

else:
		import sys

		__a    :	Any         =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)